The training included in this career development award promotes the applicant's development as a physician-scientist during his the PhD phase of his MD/PhD training in transdisciplinary computational genomics. The applicant has previously completed a Master's Degree in Mathematics, and has had extensive instruction in general computational biology. This unique melding of high-level computational expertise and interest in applied genomics has led to the development of this innovative project. He is now being co-mentored by Drs. Barash and Bhoj to gain complementary practical training in predictive algorithm development and molecular genetics. In both his clinical and research interests he is dedicated to improving the rate of molecular diagnosis for children with rare Mendelian disorders. His short-term goals include developing and refining his skills in RNA splicing prediction and human genetic variation analysis in exome and genome data. In addition, he will gain new insight into experimental design, data interpretation, and scientific communication skills to ensure his successful post-doctoral transition. His co-mentors for the proposal are Drs. Yoseph Barash and Elizabeth Bhoj, international leaders in computational genomics and molecular genetics. In addition he will be supported by outstanding resources of the MSTP at Penn, which has an extensive proven track record of successful previous awardees. The applicant has been pursuing work in creating an improved computational pipeline for the analysis of variants from exome and genome data. Specifically he is capturing the intronic and synonymous variants that are generally removed from the analysis pipeline because of the difficulty in determining the pathogenicity of such variants. As there are many intronic and synonymous variants that are known to cause Mendelain disorders, this clearly leads to missed diagnoses. In Aim 1 he will generate an interpretable algorithm for prioritizing general splicing variants that guides functional validation. In Aim 2 he will identify novel variants and genes for mechanistic evaluation in the pathogenesis of congenital anomalies. This algorithm will be generally applicable, significantly enhancing our ability to provide molecular diagnoses for all patients with suspected Mendelian disorders. In addition, this proposal will allow the candidate to gain experience, knowledge, and new skills to successfully lay the foundation as a physician-scientist in computational genomics.